The prevalent use of computers, smart-phones, tablets, and other electronic devices generates an ever-increasing demand for digital security. Traditional means for securing digital devices include passwords and personal identification numbers (PINs). Such traditional security means are associated with a number of issues, for example passwords and PINs may be stolen, lost, or forgotten.
As electronic devices become more technologically advanced, new means for digital security have been created. Biometric security systems, such as fingerprint-recognition systems, are one approach to digital security. Biometric traits like fingerprints, iris, and face are increasingly being used for identification and access control. The use of biometrics has significant advantages compared to traditional methods like passwords and PINs. Unlike passwords and PINs, biometrics ordinarily cannot be stolen, lost, or forgotten. Among the different biometrics, fingerprints are very popular and have a number of strengths. Fingerprints are unique to every individual, non-invasive to acquire, and do not change with time.
Fingerprint images have a pattern of black and white regions called as ridges and valleys. Ridges correspond to the upper layer of skin on the fingerprint, and valleys correspond to the lower layer of the skin. Depending on the ridge pattern, a fingerprint can be classified to one of five categories: left loop, right loop, arch, tented arch, and whorl. The ridge patterns in loop images enter the fingerprint from one side, form a loop, and exit from the same side. Ridges in arch images start from one side of the finger, form an arch shape in the center region, an exit from the other side. Tented arch images are similar to arches, but the ridges have a sharper rise and are discontinuous in the center region. Whorl images consist of ridges, which turn around by entire 360 degrees.
Example embodiments described herein involve an algorithm for single-finger matching. Other embodiments described herein involve multi-finger matching. Multi-finger matching is similar to single-finger matching, with one additional step of fusing match scores obtained by applying the algorithm on individual fingers. The fusion algorithm can be a simple sum of scores obtained from individual fingers, or a more sophisticated algorithm.
Fingerprint matching problems are of two types, verification and identification. Verification, i.e., one-to-one matching, verifies that a person is who they claim to be. Verification is often done by matching the subject's fingerprints with a previously stored template. Identification, i.e., one-to-many matching, identifies a person by matching his or her fingerprints against a database of fingerprints. The database of fingerprints is called a gallery and the input fingerprint being searched is called a probe. Such a database may be stored on a hard-drive storage system and may be connected to and accessible from a network and/or server. A network connected fingerprint database may allow a number of devices in a number of locations to input probe fingerprints to be matched across a shared database.
Multi-stage matching is often used to search a large database of fingerprints. A first stage of filtering should filter out a larger group of candidates to be searched through in later stages wherein a more accurate search may be performed. Earlier stages typically have a high speed, and pass a small subset of a larger number of candidates to later stages. Later stages typically have a lower speed, and a very high accuracy. The filtering of candidates in the initial stages is often based on a global ridge pattern. Fingerprint images are first aligned using a reference point.
Typically, a core point is used as the reference point. The core point of a fingerprint is defined as the north-most point of the innermost ridge line. Once the images are registered, their ridge pattern is modeled, and used to identify candidates to be passed to the next stage. The candidates are usually fingerprints in which the ridge pattern are of the same category as the probe.
While issues with traditional means for security have been addressed with new means, new methods for digital security, including fingerprint-recognition for example, come with new issues. For example, methods relying on core-point detection and global ridge pattern have a number of limitations. Accurate detection of core-point location is a non-trivial problem, and some fingerprint images, e.g., arch images, do not have a core point. Selecting candidates based merely on the category of the ridge pattern may not result in a high filtering rate, because fingerprints are not evenly distributed between different categories. For example, loop and whorl images constitute nearly 60% and 30% of the total fingerprint images respectively.